Purpose: Small-cell lung cancer (SCLC) is difficult to cure. In this study, the SEER database was used to construct a model and explore the potential prognostic factors of SCLC patients.
Methods: The data were sorted out and randomly divided into training cohort and verification cohort. Univariate and multivariate Cox regression were used in the training cohort to analyze the independent prognostic factors, then they be incorporated into the Nomogram model. Using the C-index, calibration algorithm and ROC in conjunction with the risk scores, the model was verified with the verification cohort. Finally, the overall survivals of those factors were evaluated in the total cases.
Results: In the training cohort, we found that age, race, sex, total stage and extension were independent factors which were included in the Nomogram model. C-index(s) that obtained from the training and verification cohorts showed that the model has predictive power. Moreover, the calibration curves and AUC results proved that the model is of great consistency not only in the training cohort but also in the verification cohort. Finally, significant differences in survival were observed among the above-mentioned factors and the overall survivals decreased over time.
Conclusions: Age, race, sex, total stage and extension degree are independent risk factors for overall survival of patients. The Nomogram model can better predict the 1-year, 3-year and 5-year survival probabilities, providing accurate reference for clinical individualized treatment.